Training Data Governance for Brain Foundation Models

TL;DR

This paper explores the development and ethical considerations of brain foundation models trained on neural data, emphasizing the need for governance and safeguards due to their sensitive nature.

Contribution

It introduces the concept of brain foundation models, analyzes their unique data governance challenges, and proposes ethical frameworks and safeguards for responsible development.

Findings

Neural data used in models require stronger privacy protections.

Current practices risk misuse due to fragmented governance.

Ethical safeguards are essential for responsible deployment.

Abstract

Brain foundation models bring the foundation model paradigm to the field of neuroscience. Like language and image foundation models, they are general-purpose AI systems pretrained on large-scale datasets that adapt readily to downstream tasks. Unlike text-and-image based models, however, they train on brain data: large-datasets of EEG, fMRI, and other neural data types historically collected within tightly governed clinical and research settings. This paper contends that training foundation models on neural data opens new normative territory. Neural data carry stronger expectations of, and claims to, protection than text or images, given their body-derived nature and historical governance within clinical and research settings. Yet the foundation model paradigm subjects them to practices of large-scale repurposing, cross-context stitching, and open-ended downstream application. Furthermore, these practices are now accessible to a much broader range of actors, including commercial developers, against a backdrop of fragmented and unclear governance. To map this territory, we first describe brain foundation models' technical foundations and training-data ecosystem. We then draw on AI ethics, neuroethics, and bioethics to organize concerns across privacy, consent, bias, benefit sharing, and governance. For each, we propose both agenda-setting questions and baseline safeguards as the field matures.